Instructions to use GENG/hubert_5000_checkpoint_ls with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GENG/hubert_5000_checkpoint_ls with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="GENG/hubert_5000_checkpoint_ls")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("GENG/hubert_5000_checkpoint_ls") model = AutoModelForCTC.from_pretrained("GENG/hubert_5000_checkpoint_ls", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download scaler.pt from GENG/hubert_5000_checkpoint_ls: direct link, hf CLI and curl.
- Browser
- Download file 559 Bytes
-
https://huggingface.co/GENG/hubert_5000_checkpoint_ls/resolve/main/scaler.pt
- Command line
-
hf download hf://GENG/hubert_5000_checkpoint_ls/scaler.pt
-
curl -L -o scaler.pt https://huggingface.co/GENG/hubert_5000_checkpoint_ls/resolve/main/scaler.pt
559 Bytes
- Xet hash:
- 569e7b2ae4ebe8200b52e046502df990b84ed8f563ac712bba6400016ee50186
- Size of remote file:
- 559 Bytes
- SHA256:
- 45184de53a499f66ddfa3fab375907b51d703d0d847062055249133cf7ccfa0e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.